Coding agents aren't winning because of better models — they're winning because CLI-based tools like Claude Code manage context better than any IDE. The real productivity unlock comes from sub-agent architecture, aggressive context clearing, and treating tests as the verification loop that lets agents run fast without breaking everything.

Most MCP servers expose raw REST endpoints to agents that can't afford to browse them. Designing agent-native tool surfaces — curated, outcome-oriented, token-aware — separates production-grade integrations from expensive handshake failures.

Amazon Kiro replaces ad-hoc prompting with a spec-driven workflow: structured EARS requirements, correctness properties, and property-based tests. The result is AI-generated code you can actually verify against its original intent.

Stanford research across 120k developers shows median AI coding ROI of just 10%, despite millions in tool spending. The variance between teams is massive—and telling.

Coding evals jumped from single lines to full codebases in four years. Static benchmarks miss the production gap that destroys rollout timelines.